Scope and Topics

In multiobjective optimization problems, there may exist two or more distinct Pareto optimal sets
(PSs) corresponding to the same Pareto Front (PF). These problems are defined as multimodal
multiobjective optimization problems (MMOPs). Arguably, finding one of these multiple PSs may be
sufficient to obtain an acceptable solution for some problems. However, failing to identify more
than one of the PSs may prevent the decision maker from considering solution options that could
bring about improved performance.

The aim of this special session is to promote the research on MMO and hence motivate researchers
to
formulate real-world practical problems. Given that the study of multimodal multiobjective
optimization (MMO) is still in its emerging stages, although many real-word applications are
likely
to be amenable to treatment as a MMOP, to date the researchers have ignored such formulations:

This special session is devoted to the novel approaches, algorithms and techniques for solving
MMOPs. The main topics of the special session are:

Submission instructions

MMO Benchmark for CEC2019 and corresponding comparison instruction can be found on
http://www5.zzu.edu.cn/ecilab/info/1036/1163.htm.
Papers should be submitted following the
instructions at the IEEE CEC 2019 web site before the deadline. Please select the main research
topic as the Special Session on “multimodal multiobjective optimization”. Accepted papers will be
included and published in the conference proceedings. Participants without paper are also welcomed
and a detailed report about the algorithm and results in IEEE format should be provided.

Scope and Topics

The human possesses the most remarkable ability to manage and execute multiple tasks simultaneously,
e.g., talking while walking. This desirable multitasking capability has inspired computational
methodologies and approaches to tackle multiple tasks at the same time by leveraging commonalities
and differences across different tasks to improve the performance and efficiency of resolving
component tasks compared to when dealing with them separately. As a well-known example, multi-task
learning is a very active subfield of machine learning whereby multiple learning tasks are
performed together using a shared model representation such that the relevant information contained
in related tasks can be exploited to improve the learning efficiency and generalization performance
of task-specific models.

Multi-task optimization (MTO) is a newly emerging research area in the field of
optimization, which investigates how to effectively and efficiently tackle multiple optimization
problems at the same time. In the multitasking scenario, solving one optimization problem may assist
in solving other optimization problems (i.e., synergetic problem-solving) if these problems bear
commonality and/or complementarity in terms of optimal solutions and/or fitness landscapes. As a
simple example, if some problems have the same globally optimal solution but distinct fitness
landscapes, obtaining the global optimum to any problem makes the others also get solved. Recently,
an evolutionary MTO paradigm named as evolutionary multitasking was proposed to explore the
potential of evolutionary algorithms (EAs) incorporated with a unified solution representation space
for MTO. As a population-based optimizer, EAs feature the Darwinian “survival-of-the-fittest”
principle and nature-inspired reproduction operations which inherently promote implicit knowledge
transfer across tasks during problem-solving. The superiority of this new evolutionary multitasking
framework over traditional ways of solving each task independently has been demonstrated on
synthetic and real-world MTO problems by using a multi-factorial EA (MFEA) developed under
this framework.

Evolutionary multitasking opens up new horizons for researchers in the field of evolutionary
computation. It provides a promising means to deal with the ever-increasing number, variety and
complexity of optimization tasks. More importantly, rapid advances in cloud computing could
eventually turn optimization into an on-demand service hosted on the cloud. In such a case, a
variety of optimization tasks would be simultaneously executed by the service engine where
evolutionary multitasking may harness the underlying synergy between multiple tasks to provide
service consumers with faster and better solutions.

Due to the good response of this competition held at CEC’17 and WCCI’2018 (17 entries in CEC’17, and
13 entries in WCCI’18), we would like to continue to organize this competition at CEC’19, aiming at
promoting research advances in both algorithmic and theoretical aspects of evolutionary MTO.

Submission instructions

Interested participants are strongly encouraged to report their approaches and results in a paper and
submit it to "CEC-01 Special Session on
Memetic Computing" before the CEC 2019 paper submission
deadline
If you would like to participate in the competition, please kindly inform us about your interest via
email (mtocompetition@gmail.com) so that we can update you about any bug fixings and/or the
extension of the deadline.

Scope and Topics

Evolutionary multi-objective optimization (EMO) has been flourishing for two decades in academia.
However, the industry applications of EMO to real-world optimization problems are infrequent, due to
the strong assumption that objective function evaluations are easily accessed. In fact, such
objective functions may not exist, instead computationally expensive numerical simulations or costly
physical experiments must be performed for evaluations. Such problems driven by data collected in
simulations or experiments are formulated as data-driven optimization problems, which pose
challenges to conventional EMO algorithms. Firstly, obtaining the minimum data for conventional EMO
algorithms to converge requires a high computational or resource cost. Secondly, although surrogate
models that approximate objective functions can be used to replace the real function evaluations,
the search accuracy cannot be guaranteed because of the approximation errors of surrogate models.
Thirdly, since only a small amount of online data is allowed to be sampled during the optimization
process, the management of online data significantly affects the performance of algorithms. The
research on data-driven evolutionary optimization has not received sufficient attention, although
techniques for solving such problems are highly in demand. One main reason is the lack of benchmark
problems that can closely reflect real-world challenges, which leads to a big gap between academia
and industries.

Submission instructions

In this competition, we carefully select 6 benchmark multi-objective optimization problems from
real-world applications, including design of car cab, optimization of vehicle frontal structure,
filter design, optimization of power systems, and optimization of neural networks. The objective
functions of those problems cannot be calculated analytically, but can be calculated by calling an
executable program to provide true black-box evaluations for both offline and online data sampling.
A set of initial data is generated offline using Latin hypercube sampling, and a predefined fixed
number of online data samples are set as the stopping criterion. This competition, as an event
organized by the Task Force on “Intelligence Systems for Health” in the Intelligent Systems
Application Technical Committee and Task Force on “Data-Driven Evolutionary Optimization of
Expensive Problems” in the Evolutionary Computation Technical Committee, aims to promote the
research on data-driven evolutionary multi-objective optimization by suggesting a set of benchmark
problems extracted from various real-world optimization applications. All benchmark functions are
implemented in MATLAB code. Also, the MATLAB code has been embedded in a recently developed software
platform – PlatEMO, an open source MATLAB-based platform for evolutionary multi- and many-objective
optimization, which currently includes more than 50 representative algorithms and over 100 benchmark
functions, along with a variety of widely used performance indicators.

Submission deadline

Scope and Topics

To shape a low carbon energy future has been a crucial and urgent task under Paris Global Agreement.
Numerous optimisation problems have been formulated and solved to effectively save the fossil fuel
cost and relief energy waste from power system and energy application side. However, some key
problems are of strong non-convex, non-smooth or mixed integer characteristics, leading to
significant challenging issues for system operators and energy users. This competition aims to
encourage the relevant researchers to present their state-of-the-art optimisation tools for solving
three featured complicated optimisation tasks including unit commitment, economic load dispatch and
parameter identification for photovoltaic models and PEV fuel cells.

Unit commitment (UC) problem aims to minimize the economic cost by optimally determining the
online/offline status and power dispatch of each unit, while maintaining various system constraints,
formulating a large scale mixed-integer problem. Economic load dispatch is a power system operation
task aiming to minimise the fossil fuel economic cost by determining the day-ahead and/or hourly
power generation for each power generator. Fuel cell is one of most important energy storages in the
future, particularly with the applications to vehicles and robotics. Proton Exchange Membrane is the
key component of fuel cell however is of significant difficulties to be accurately modelled due to
the nonlinearity, multivariate and strongly coupled characteristics. Evolutionary computation is
immune from complex problem modelling formulation, and is therefore promising to provide powerful
optimisation tools for intelligently and efficiently solving problems such as smart grid and various
energy systems scheduling to reduce carbon consumptions.

A brief list of potential submission topics is shown below:

Unit commitment

Economic load dispatch

Parameters identification for photovoltaic models and PEM fuel
cells

Submission instructions

This competition intends to reflect the state-of-the-art advances of evolutionary optimisation
approaches for solving emerging problems in complex modern power and energy system. In this
competition, we choose the above three questions as the optimization object, in order to make it
easier for comparative studies of different algorithms using the same platform, and get the better
optimization results. The simulate experiment and data should be expressed on MATLAB platforms or
other software platforms, therefore be ranked by the results according to the competition evaluation
criteria. Interested participants are strongly encouraged to report their approaches and results in
a paper and submit it to our special session CEC-17
Special Session on Evolutionary Computations on
Smart Grid and Sustainable Energy Systems in the conference submission system, and also send
their
codes to the competition organizer at zl.yang@siat.ac.cn
for verification. All the papers should be
submitted before the conference paper submission deadline.

Scope and Topics

Following the success of the previous edition at WCCI 2018, we are relaunching this competition at major
conferences in the field of computational
intelligence. This CEC 2019 competition proposes optimization of a centralized day-ahead energy
resource management problem in smart grids under environments with uncertainty. This year we
increased the difficulty by proving a more challenging case study, namely with higher degree of
uncertainty.

Competition goals:

The CEC 2019 competition on “Evolutionary Computation in Uncertain Environments: A Smart Grid
Application” has the purpose of bringing together and testing the more advanced Computational
Intelligence (CI) techniques applied to an energy domain problem, namely the energy resource
management problem under uncertain environments. The competition provides a coherent framework where
participants and practitioners of CI can test their algorithms to solve a real-world optimization
problem in the energy domain with uncertainty consideration, which makes the problem more
challenging and worth to explore.

Since the proposed algorithms might have distinct sizes of population
and run for a variable
number of iterations, a maximum number of “50000 function evaluations” is allowed in each trial
for all participants. The convergence properties of the algorithms are not a criterion to be
qualified in this competition.

20 independent trials should be performed in the framework by each
participant.

how to submit an entry and how to evaluate them

The winner will be the participant with the minimum ranking index,
which is calculated as the average value over the 20 trials of the expected fitness value (over
the considered uncertain scenarios) plus the standard deviation

Each participant is kindly requested to put the text files
corresponding to final results (see guideline document), as well as the implementation files
(codes), obtained by using a specific optimizer, into a zipped folder named CEC2019_SG_AlgorithmName_ParticipantName.zip (e.g. CEC2019_SG_DE_Lezama.zip).

Submission deadline

7th January 2019, 23:59 (GMT) (For those submitting papers to the special session)
30th April 2019, 23:59 (GMT) (Submission without paper)

Scope and Topics

Research on single objective optimization algorithms often forms the foundation for more complex
scenarios, such as niching algorithms and both multi-objective and constrained optimization
algorithms. Traditionally, single objective benchmark problems are also the first test for new
evolutionary and swarm algorithms. Additionally, single objective benchmark problems can be
transformed into dynamic, niching composition, computationally expensive and many other classes of
problems. It is with the goal of better understanding the behavior of evolutionary algorithms as
single objective optimizers that we are introducing the 100-Digit Challenge. The SIAM 100-Digit
Challenge was developed in 2002 by Nick Trefethen in conjunction with the Society for Industrial and
Applied Mathematics (SIAM) as a test for high-accuracy computing. Specifically, the challenge was to
solve 10 hard problems to 10 digits of accuracy. One point was awarded for each correct digit,
making the maximum score 100, hence the name. Contestants were allowed to apply any method to any
problem and take as long as needed to solve it. Out of the 94 teams that entered, 20 scored 100
points and 5 others scored 99. In a similar vein, we propose the 100-Digit Challenge. In contrast to
the SIAM version, this 100-Digit Challenge asks contestants to solve all ten problems with one
algorithm, although limited control parameter “tuning” for each function will be permitted to
restore some of the original contest’s flexibility. Another difference is that the score for a given
function is the average number of correct digits in the best 25 out of 50 trials.

Submission instructions

The participants are asked to submit their papers to the CEC 2019 according to the paper submission
instructions. Authors are asked to email their final results in a format requested in the associated
Technical Report. Three top performing algorithms will be made available online form the competition
web pages.

Scope and Topics

With the success of AlphaGo, there has been a lot of interest among students and professionals to
apply machine learning to gaming and in particular to the game of Go. Several conferences have held
competitions human players vs. computer programs or computer programs against each other. The goal
of this competition includes: (1)The OpenGo Darkforest (OGD) Cloud Platform for Game of Go, (2)
Understand the basic concepts of an FML-based fuzzy inference system, (3) Use the FML intelligent
decision tool to establish the knowledge base and rule base of the fuzzy inference system, (4) Use
the data predicted by Facebook AI Research (FAIR) Open Source Darkforest AI Bot as the training
data, (5) Use the data predicted by Facebook AI Research (FAIR) Open Source ELF OpenGo AI Bot as the
desired output of the training data, and (6) Optimize the FML knowledge base and rule base through
the methodologies of evolutionary computation and machine learning in order to develop a regression
model based on FML-based fuzzy inference system.

Submission instructions

The participants are invited to submit their results via the competition
website
(http://oase.nutn.edu.tw/cec2019-fmlcompetition/). Participants are also encouraged to submit the
results to the
competition held in FUZZ-IEEE 2019
(http://oase.nutn.edu.tw/fuzz2019-fmlcompetition/). We will announce the winner at both conferences.

Submission deadline

Scope and Topics

The General Video Game AI (GVG-AI) Competition explores the problem of creating agents for general
video game playing. How would you create a single agent that is able to play any game it is given?
Could you program an agent that is able to play a wide variety of games, without knowing which games
are to be played and without a forward model?

The GVGAI Learning framework has been interfaced with OpenAI Gym and provides a fantastic and
user-friendly environment for testing your Reinforcement Learning agents. The framework also allow
users to create their own games easily to test their agents.

Scope and Topics

The game is a small implementation of a Strategy Card Game, designed to perform AI research. Its
advantage over the real cardgame AI engines is that it is much simpler to handle by the agents, and
thus allows testing more sophisticated algorithms and quickly implement theoretical ideas. Its goal
is to encourage advanced research, free of drawbacks of working with the full-fledged game. It means
i.a. embedding deckbuilding into the game itself (limiting the usage of premade decks), and allowing
efficient search beyond the one turn depth.

All cards effects are deterministic, thus the nondeterminism is introduced only by the ordering of
cards and unknown opponent's deck. The game board consists of two lines (similarly as in
TES:Legends), so it favors deeper strategic thinking. Also, it is based on the fair arena mode,
i.e., before every game, both players create their decks secretly from the symmetrical yet limited
choices. Because of that, the deckbuilding is dynamic and cannot be simply reduced to using
human-created top-meta decks.

Submission instructions

The participants are invited to submit their code via email (jko@cs.uni.wroc.pl).

Submission deadline

Scope and Topics

Nonlinear equation systems (NESs) frequently arise in many physical, electronic, and mechanical
processes. Very often, a NES may contain multiple roots. Since all these roots are important for a
given NES in the real-world applications, it is desirable to simultaneously locate them in a single
run, such that the decision maker can select one final root which matches at most his/her
preference. For solving NESs, several classical methods, such as Newton-type methods, have been
proposed. However, these methods have some disadvantages in the sense that they are heavily
dependent on the starting point of the iterative process, can easily get trapped in a local optimal
solution, and require derivative information. Moreover, these methods tend to locate just one root
rather than multiple roots when solving NESs.

Solving NESs by EAs is a very important area in the community of evolutionary computation, which is
challenging and of practical interest. However, systematic work in this area is still very limited.
The aim of competition is to facilitate the development of EAs for locating multiple roots of NESs.

Submission instructions

The participants are invited to submit a paper to our special session “CEC-61 Special Session on
Evolutionary Algorithms for Nonlinear Equations Systems” in the conference submission system. Please
also send the codes and results to ywang@csu.edu.cn for verification.

Scope and Topics

In the past two decades, many evolutionary algorithms have been developed and successfully applied
for solving a wide range of optimization problems. Although these techniques have shown excellent
search capabilities when applied to small or medium sized problems, they still encounter serious
challenges when applied to large scale problems, i.e., problems with several hundreds to thousands
of variables. This is due to the Curse of dimensionality, as the size of the solution space of the
problem grows exponentially with the increasing number of decision variables, there is an urgent
need to develop more effective and efficient search strategies to better explore this vast solution
space with limited computational budgets. In recent years, research on scaling up EAs to large-scale
problems has attracted significant attention, including both theoretical and practical studies.
Existing work on tackling the scalability issue is getting more and more attention in the last few
years.

This special session is devoted to highlight the recent advances in EAs for handling large-scale
global optimization (LSGO) problems, involving single objective or multiple objectives,
unconstrained or constrained, binary/discrete or real, or mixed decision variables. More
specifically, we encourage interested researchers to submit their original and unpublished work on:

Theoretical and experimental analysis on the scalability of EAs;

Novel approaches and algorithms for scaling up EAs to large-scale
optimization problems;

Submission instructions

The competition allows participants to run their own algorithms on 15 benchmark functions, each of
them of 1000 dimensions. Detailed information about these benchmark functions is provided in the
following technical report:

Source code is available in the website, for C++, MatLab, Java and Python.

The technique and the results can be reported in a paper for the corresponding special session. The
authors must provide their results as shown in the aforementioned technical report (Table 2). In
order to make it easier to obtain the results in the requested format, the original source code of
the benchmark has been modified to automate this task (except in Java version). Additionally,
several tools are provided to create an Excel file with the results as recorded by the modified code
and the latex table to allow its easy inclusion in the paper.

In order to help researchers to compare their proposals with previous winners, we have developed a
website https://tacolab.org, that allows researchers to compare the data of their proposal (provided
in an Excel file) with those of previous algorithms. Several reports, both as tables and figures,
can be automatically generated by this tool (and exported to be included in the manuscript),
including, in the report LSGO Competition plots the criteria used in the competition.

Scope and Topics

The conventional divide-the-dollar game is a two player game where the players simultaneously bid on
how to divide a dollar. If the bids sum to a dollar or less each player receives their bid,
otherwise they receive nothing. This contest is based on the generalized divide the dollar game,
which has N ≥ 2 players. In this game, instead of dividing a dollar, a scoring set, S ⊂ RN is used.
Each player bids a point coordinate and, if the resulting point is in the scoring set, then the
players receive their bid, otherwise nothing. The players will be given several example sets,
similar to optimization problems in an optimization contest, to train a general purpose agent for
learning a generalized divide the dollar problem from feedback. Each participant will upload an
agent to play a generalized divide the dollar game.

The contest will use sets not seen by the players before and will be restricted to the two-player
version. All sets satisfy x,y ∈ R2 with X ≥ 0, y ≥ 0, and x,y ≤ 2. Sets will consist of one or more
simply connected regions. Agents will participate in a round-robin tournament with the score on each
set recorded. During play the players will be given feedback in the form of each players bid and the
outcome (score/no score). Agents will also have access to the history of bids each agent has made
and if that bid scored. Winners will be determined for each problem test set and an overall winner
with the best average score over all of the problem test sets. Agents can be designed using any
computational intelligence technique. Contest participants will upload a framework in Java for their
agent through the competition website. The uploaded agent must be a standalone agent. Each
participant may submit only one agent to the contest. Each participant is expected to submit a short
paper (23 pages) describing their agents structure and the computational intelligence methods used
to construct and train it. Papers will be orally presented during the special session on games and
will appear in the conference proceedings. Winners will be announced during the special session.

This contest is intended as a successor to the contests for prisoner’s dilemma, with generalized
divide the dollar being a more complex game with a far larger strategy space. The contest organizers
have published at least one agent representation that can play this game, but adapting to unknown
scoring sets is a challenge that is likely to spark research in agent representations and advance
the theory and practice of mathematical games in evolutionary computation.

Submission instructions

TBA

Submission deadline

Scope and Topics

The participants are invited to use the Nevergrad platform to implement an optimizer and evaluate it
against currently implemented algorithms. 5 different tracks are proposed: Noisy, Ill Conditioned,
Deceptive, Parallel, One-shot.

Submission instructions

The participants are invited to implement their algorithm(s) in the cec2019_optimizer.py file, and
then run a command line which will test it against different settings, and plot results figures.
The figures and implementation should then be sent to the organizers by e-mail.